A machine-learning model based on dynamic contrast-enhanced MRI for preoperative differentiation between hepatocellular carcinoma and combined hepatocellular-cholangiocarcinoma.

Aim: To establish a machine-learning model based on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to differentiate combined hepatocellular-cholangiocarcinoma (cHCC-CC) from hepatocellular carcinoma (HCC) before surgery.
Materials and Methods: Clinical and MRI data of 194 patients with histopathologically diagnosed cHCC-CC (n=52) or HCC (n=142) were analysed retrospectively. ITK-SNAP software was used to delineate three-dimensional (3D) lesions and extract high-throughput features. Feature selection was carried out based on Pearson's correlation coefficient and least absolute shrinkage and selection operator (LASSO) regression analysis. A radiomics model (radiomics features), a clinical model (i.e., clinical-image features), and a fusion model (i.e., radiomics features + clinical-image features) were established using six machine-learning classifiers. The performance of each model in distinguishing between cHCC-CC and HCC was evaluated with the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), sensitivity, and specificity.
Results: Significant differences in liver cirrhosis, tumour number, shape, edge, peritumoural enhancement in the arterial phase, and lipid were identified between cHCC-CC and HCC patients (p<0.05). The AUC of the fusion model based on logistic regression was 0.878 (95% CI: 0.766-0.949) in the arterial phase in the test set, and the sensitivity/specificity was 0.844/0.714; however, the AUC of the clinical and radiomics models was 0.759 (95% CI: 0.663-0.861) and 0.838 (95% CI: 0.719-0.921) in the test set, respectively.
Conclusion: The fusion model based on DCE-MRI in the arterial phase can significantly improve the diagnostic rate of cHCC-CC and HCC as compared with conventional approaches.
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